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Drug and Alcohol Dependence
j o u r n a l h o m e p a g e :
w w w . e l s e v i e r . c o m / l o c a t e / d r u g a l c d e p
Cannabis use and development of externalizing and internalizing behaviour
problems in early adolescence: A TRAILS study
M.F.H. Griffith-Lendering
a
,∗
, S.C.J. Huijbregts
a
, A. Mooijaart
b
, W.A.M. Vollebergh
c
, H. Swaab
a
aDepartment of Clinical Child and Adolescent Studies- Neurodevelopmental Disorders, Faculty of Social Sciences, Leiden University, P.O. Box 9555,2300 RB Leiden, The Netherlands
bDepartment of Psychology, Faculty of Social Sciences, Leiden University, P.O. Box 9555, 2300 RB Leiden, The Netherlands cDepartment of Interdisciplinary Social Science, University of Utrecht, P.O. Box 80.140, 3508 TC Utrecht, The Netherlands
a r t i c l e i n f o
Article history:Received 25 March 2010 Received in revised form 17 September 2010 Accepted 1 November 2010
Available online 3 January 2011 Keywords:
Adolescence Cannabis
Externalizing and internalizing problems
a b s t r a c t
Aim:To examine the prospective relationship between externalizing and internalizing problems and cannabis use in early adolescence.
Materials and Methods: Data were used from the TRAILS study, a longitudinal cohort study of (pre)adolescents (n= 1,449), with measurements at age 11.1 (T1), age 13.6 (T2) and age 16.3 (T3). Inter-nalizing (withdrawn behaviour, somatic complaints and depression) and exterInter-nalizing (delinquent and aggressive behaviour) problems were assessed at all data waves, using the Youth Self Report. Participants reported on cannabis use at the second and third wave. Path analysis was used to identify the temporal order of internalizing and externalizing problems and cannabis use.
Results:Path analysis showed no associations between cannabis use (T2-T3) and internalizing problems (T1-2-3). However, cannabis use and externalizing problems were associated (rranged from .19–.58); path analysis showed that externalizing problems at T1 and T2 preceded cannabis use at T2 and T3, respectively. In contrast, cannabis use (T2) did not precede externalizing problems (T3).
Conclusions:These results suggest that in early adolescence, there is no association between internalizing behaviour and cannabis use. There is an association between externalizing behaviour and cannabis use, and it appears that externalizing behaviour precedes cannabis use rather than the other way around during this age period.
© 2010 Elsevier Ireland Ltd.
1. Introduction
Regular cannabis use has been associated with a wide range of
mental health problems including psychotic disorders (
Arseneault
et al., 2002; Moore et al., 2007
), externalizing problems (aggressive
and delinquent behaviour) (
Fergusson et al., 2002; Monshouwer
et al., 2006
) and, to a lesser extent, internalizing problems, such
as depression (
Degenhardt et al., 2001; Degenhardt et al., 2003;
Patton et al., 2002
) and anxiety (
Patton et al., 2002; van Laar et al.,
2007; Hayatbakhsh et al., 2007a
). Several hypotheses have been
put forward to explain these associations, including the “damage
hypothesis”, which proposes that cannabis use precedes
men-tal health problems (
Brook et al., 1998; Kandel et al., 1992
) and
the “self medication hypothesis”, which proposes that individuals
with mental health problems tend to resort to drug use to sooth
their problems (
Khantzian, 1985
). The “shared causes
hypothe-sis” proposes that the linkage between cannabis use and mental
∗Corresponding author. Tel.: +31 71 5276623; fax: ++31 71 5273619. E-mail address:[email protected](M.F.H. Griffith-Lendering).
health problems is the result of genetic and environmental
fac-tors associated with both problem behaviour and cannabis use
(
Fergusson and Horwood, 1997; Fergusson et al., 2002; Shelton
et al., 2007
).
Shared causes are often found for externalizing behaviour and
cannabis use (
Fergusson and Horwood, 1997; Fergusson et al.,
2002
). Several studies have shown substantially weaker
associ-ations between cannabis use and externalizing behaviour after
statistical control for factors such as social economic status and
use of other substances (e.g.
Korhonen et al., 2010
). However, most
studies do show some residual variance in associations between
externalizing behaviour and cannabis use that cannot be explained
by environmental factors (
Fergusson et al., 2007; Fergusson et al.,
2002; Pedersen et al., 2001
). The temporal order of cannabis use
and both externalizing and internalizing behaviour has not yet
been disentangled (
Fergusson et al., 2002; Monshouwer et al.,
2006
). Most longitudinal evidence supports the self-medication
hypothesis, which states that externalizing problems precede the
use of cannabis at this age (
King et al., 2004; Fergusson et al.,
2007; Pedersen et al., 2001
). There is also evidence to suggest
that externalizing behaviour during adolescence precedes cannabis
0376-8716© 2010 Elsevier Ireland Ltd.doi:10.1016/j.drugalcdep.2010.11.024
Open access under the Elsevier OA license.
use in early adulthood (
Hayatbakhsh et al., 2007b
). Although it is
difficult to control for all potential confounders simultaneously,
some of these studies did not control for important potential
confounders, such as SES, use of other substances and parental
psychopathology, and therefore may have left open the
possi-bility of shared causes more than necessary. For internalizing
behaviour, the relationship is even more complex: firstly, compared
to externalizing behaviour problems, there is less evidence for
an association between cannabis use and internalizing behaviour
problems (
Monshouwer et al., 2006
). In several studies that did
initially find a significant association between cannabis use and
internalizing behaviour, the association became non-significant
after statistical control for confounding variables (
Harder et al.,
2008; McGee et al., 2000
). Nonetheless, there are some studies
that have found evidence for the self-medication hypothesis, with
internalizing behaviour problems preceding cannabis use at later
age (
King et al., 2004; Wittchen et al., 2007
). Again, shared causes
cannot be ruled out, as the associations may be explained by
residual confounding (
Fergusson and Horwood, 1997; Fergusson
et al., 2002; Hayatbakhsh et al., 2007a
). There is also
(contrast-ing) evidence suggesting that internalizing behaviour in young
adolescence is not related to substance use at a later age,
includ-ing the use of cannabis (
Alati et al., 2008; Hayatbakhsh et al.,
2008; Ferdinand et al., 2001
). Thus, in general, evidence regarding
(the direction of) associations between cannabis use and
inter-nalizing/externalizing behaviour problems in adolescence is not
yet convincing, which is mainly due to the fact that most studies
did not analyze temporally bi-directional associations (i.e., where
cannabis use can precede but also follow behaviour problems),
and which might also partly be due to the fact many studies
did not control comprehensively for potentially confounding
vari-ables.
It is important to study associations between
externaliz-ing and internalizexternaliz-ing problems on the one hand and cannabis
use on the other during early adolescence for several reasons.
Firstly, early adolescence is a life phase characterised by rapid
biological changes and consecutive maturation processes. These
developmental processes might increase vulnerability for
endur-ing effects of external influences like use of cannabis (
Schneider,
2008
). Secondly, cannabis use usually starts in early
adoles-cence (
Monshouwer et al., 2005
), possibly because of increases
in peer-influenced risk-taking behaviours (e.g.
Fergusson and
Horwood, 1997
). So this appears to be the best possible time
to collect behavioural data antedating initiation of cannabis use.
The study of associations between internalizing and
externaliz-ing behaviours and cannabis use durexternaliz-ing early adolescence may
thus help identifying individuals who are at an increased risk for
multiple simultaneous problems (e.g. aggression and substance
use), which have been associated with the poorest long-term
outcomes. At this stage it might still help targeting one of the
problems (preferably the one that occurs first in time) in order
to prevent other or combined problems. In the present study, we
investigated relations between both internalizing and
externaliz-ing behaviour problems and cannabis use in a large population
sample of young adolescents enrolled in the Tracking
Adoles-cents’ Individual Lives Survey (TRAILS,
Huisman et al., 2008
). Using
path analysis, we investigated the temporal order of the
associ-ation between cannabis use and internalizing and externalizing
behaviour, thereby controlling for confounding factors to
elimi-nate, to some extent, the effect of shared causes. It was expected
that the link between internalizing behaviour and cannabis use
would be weaker than the association between externalizing
behaviour and cannabis use. In addition, based on findings to date,
it was expected that internalizing and externalizing behaviour
problems would precede cannabis use and not the other way
around.
2. Method
2.1. Sample
Data were gathered from participants in the Tracking Adolescents’ Individual Lives Survey (TRAILS), a prospective cohort study among adolescents in the gen-eral Dutch population. TRAILS investigates the development of mental and physical health from preadolescence into adulthood (de Winter et al., 2005). The study cov-ers biological, psychological and sociological topics and collects data from multiple informants. Participants come from five municipalities, including both urban and rural areas, in the North of the Netherlands. So far, three data collection waves have been completed: T1 (2001–2002), T2 (2003–2004) and T3 (2005–2007). Participants will be followed until (at least) the age of 24.
Of all individuals asked to participate in TRAILS (N= 2935), 76,0% agreed to par-ticipate at T1 (N= 2230; mean age 11.09 years; SD 0.55; 50.8% girls). At T2, 96.4% of these participants (N= 2149) were re-assessed. T3 was completed with 81.4% of the original number of participants (N= 1816), mean age 16.27 years; SD 0.73 (52.3% girls). At T3, 42 subjects were unable to participate in the study, due to men-tal/physical health problems, death, emigration, detention or by being untraceable. With these subjects left out, response rate increases to 83.0%. More detailed infor-mation on the selection procedures and non-response bias can be found elsewhere (de Winter et al., 2005; Huisman et al., 2008). Analyses in the present study were based on 1.449 adolescents (53.3% girls, 46.7% boys) with non-missing data on all variables of interest (described below).
2.2. Measures
2.2.1. Cannabis use.Cannabis use was assessed at T2 and T3 by self-report ques-tionnaires filled out at school, supervised by TRAILS assistants. Confidentiality of the study was emphasized so that adolescents were reassured that their parents or teachers would not have access to the information they provided. Among others, participants were asked about lifetime use and use in the last year with the follow-ing questions: ‘How often have you used cannabis in your life/in the last year’, with answer categories: ‘I have never used’, ‘used it once’, ‘used it twice’, ‘three times’,..., ‘10 times’, ‘11–19 times’, ‘20–39’ times, ‘40 times or more’). Items were recoded into five categories; (1) those who had never used; (2) those who had used but not during the past year (discontinued use); (3) those who used once or twice during the past year (experimental use); (4) those who reported using cannabis between 3 and 39 times during the past year (regular use); and (5) those who reported using it 40 times or more during the last year (heavy use). The construction of these categories was similar to that used in other studies investigating cannabis use and mental health in young adolescents (e.g.Monshouwer et al., 2006).
2.2.2. Behaviour problems..Internalizing and externalizing behaviour were assessed with the Youth Self Report (YSR), which is one of the most commonly used self report questionnaires in current child and adolescent psychiatric research (Achenbach, 1991; Verhulst and Achenbach, 1995). The YSR contains 112 items on behavioural and emotional problems in the past 6 months. Participants can rate the items as being not true (0), somewhat or sometimes true (1), or very or often true (2). The YSR covers the following domains: anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention (hyperactivity) problems, aggressive behaviour, and rule-breaking behaviour. For the present study, we used two broad-band dimensions of the YSR (Achenbach, 1991): (a) internalizing problems, consisting of items measuring anxious/depressed, with-drawn/depressed, and somatic complaints; and (b) externalizing problems, with items measuring aggressive and rule-breaking behaviour.
2.3. Control variables
Since SES, use of other substances and parental psychopathology have been shown to be among the most important correlates of cannabis use and both inter-nalizing and exterinter-nalizing behaviour (Fergusson and Boden (2008)), it was examined whether these should be included in the path analyses.
2.3.1. Socio-economic status.Socio-economic status was assessed at T1 using a 5 point scale consisting of five variables: educational level (father/mother), occupa-tion (father/mother), and family income. The internal consistency of this measure is satisfactory (Cronbach’s alpha 0.84;Veenstra et al., 2006).
2.3.2. Parental psychopathology.Parental psychopathology (i.e. depression, anxiety, substance abuse, antisocial behaviour, and psychosis) was measured by means of the Brief TRAILS Family History Interview (Ormel et al., 2005), administered at T1. Each syndrome was introduced by a vignette describing its main symptoms and followed by a series of questions to assess lifetime occurrence, professional treat-ment, and medication use. The scores for substance abuse and antisocial behaviour were used to construct a familial vulnerability index for externalizing disorder. The scores for depression and anxiety disorder were used to construct an index for internalizing disorder. The construction of a familial vulnerability index was based onKendler et al. (2003), who performed multivariate twin modelling to investi-gate shared genetic risk factors for psychiatric and substance use disorders. More
information on the construction of familial vulnerability within TRAILS is described elsewhere (Veenstra et al., 2005). For both internalizing and externalizing disor-der, parents were assigned to one of the following categories: (0) = (probably) not; (1) = (probably) yes, (2) = yes and treatment/medication (substance abuse, depres-sion, and anxiety) or picked up by police (antisocial behaviour).
2.3.3. Other substances.In order to assess alcohol and tobacco use, participants filled out a questionnaire at both T2 and T3 on the frequency of use in the past month. For tobacco use reported frequency was recoded into non-weekly (0) versus weekly (1), and for alcohol use, the reported frequency was recoded into non-monthly (0) versus non-monthly use (1). These categories were similar to those used in other studies investigating cannabis use and mental health in young adolescents (e.g.Monshouwer et al., 2006).
2.4. Data analysis
It was first examined whether non-responders differed from responders on SES (by means oft-test) and gender (by means of Pearson2-test). Next, it was examined
whether, among the responders, there were differences between cannabis users and non-users with respect to SES, familial vulnerability for internalizing and external-izing behaviour, use of alcohol and tobacco and gender (using Pearson Chi-square analysis for alcohol, tobacco use and gender and t-tests or GLM univariate analysis of variance for SES and familial vulnerability). These analyses were performed in order to determine which variables should be included in the main analyses as covariates. The temporal order of occurrence of cannabis use and internalizing and external-izing behaviour was investigated using path analyses. In path analysis, an extension of the regression model, the regression weights predicted by the model are com-pared with the observed correlation matrix for the variables, and a goodness of fit statistic is calculated. The path coefficient is a standardized regression coefficient (beta) indicating the effect of an independent variable on a dependent variable in the path model. Thus, when the model has two or more independent variables, path coefficients are partial regression coefficients, which measure the extent of effect of one variable on another in the path model controlling for other variables, using standardized data or a correlation matrix.
Following the two step approach recommended byAnderson and Gerbing (1988), confirmatory factor analysis (CFA) was used to investigate how well our hypothesized models fit the actual data. These models were based on previous research to assess temporal order of internalizing and externalizing behaviour (T1-T2-T3) and cannabis use ((T1-T2-T3) (e.g.Fergusson et al., 2002; McGee et al., 2000). In the path analyses, both internalizing and externalizing behaviour were intro-duced as latent variables with multiple indicators. The latent variable ‘internalizing’ consisted of anxious/depressed, withdrawn/ depressed and somatic complaints. The latent variable ‘externalizing’ consisted of the indicators aggressive and delin-quency. Cannabis use was represented by one indicator (i.e., the self-report measure consisting of the following categories: (1) those who had never used; (2) those who had used but not during the past year; (3) those who used once or twice during the past year; (4) those who reported using cannabis between 3 and 39 times during the past year; and (5) those who reported using it 40 times or more during the last year (see section2.2.1).
Next, we modelled prospectively cannabis use and internalizing/ externalizing identified in the CFA. Here, we included all possible associations between latent variables. To evaluate overall model fit, the root mean square error of approximation was used (RMSEA;Steiger, 1998); an RMSEA value less than .05 (Browne and Cudeck, 1993) indicates good model fit. Both2statistics and RMSEA are dependent on the
size of the sample: as we had a relatively large sample (n= 1,449), we also used the comparative fit index (CFI;Bentler, 1990) to evaluate overall model fit. A CFI value greater than .90 (Bentler, 1990) indicates good model fit.
All analyses were performed using EQS 6.1 for Windows (Bentler, 1995).
3. Results
3.1. Non-responders analysis
Responders (
n
= 1,449) and non-responders (
n
= 739) differed
in terms of SES (
t
=
−
9.6,
p
< .001); responders scored higher on
SES than non-responders (
M
= .07,
SD
= .78 vs.
M
=
−
.28,
SD
= .79).
Responders also differed from non-responders in terms of gender
(
2(1) = 10.5,
p
= .001: responders were more likely to be female
(53.3%) than non-responders (46.1%).
3.2. Descriptives
Descriptive information regarding the frequency of cannabis
use is presented in
Table 1
for participants with complete
data on all variables of interest. The number of cannabis users
increases with age as does the frequency of use. Cannabis users
Table 1
Descriptive information on cannabis use at T2 and T3 (n= 1,449).
T2 T3 Never used 93.6% (n= 1359) 69.9% (n= 1013) Discontinued use 1.4% (n= 20) 5.9% (n= 86) Experimental use 3.7% (n= 54) 10.9% (n= 158) Regular use 1.2% (n= 17) 9.6% (n= 139) Heavy use .1% (n= 2) 3.7% (n= 53)
did not differ from non-users with respect to SES (
t
(1447) =
−
.9,
p
= .387), gender (
2(1) = 1.1,
p
= .289), familial vulnerability for
internalizing (
t
(1447) =
−
.4,
p
= .705) and externalizing behaviour
(
t
(1447) =
−
1.8,
p
= .071). Cannabis users and non-users differed
sig-nificantly with respect to alcohol use at T2 (
2(1) = 90.3,
p
< .001),
alcohol use at T3 (
2(1) = 95.0,
p
< .001), tobacco use at T2 (
2
(1) = 137.3,
p
< .001) and tobacco use at T3
2(1) = 346.8,
p
< .001),
with cannabis users using alcohol and tobacco more often than
non-users (57.8% vs. 31.2% reported monthly alcohol use at T2;
percentages for T3: 94.0% vs. 70.7%; 19.8% vs. 2.2% reported weekly
tobacco use at T2; percentages for T3: 57.4% vs. 11.1%). Tobacco and
alcohol use were also related to both internalizing and
externaliz-ing behaviour and therefore included as covariates in subsequent
path analysis (for detailed information, see
Table 2
).
3.3. Path analyses: Preliminary analyses
Factor loadings of the indicators of the latent variables of
internalizing behaviour and externalizing behaviour of all three
measurement waves are presented in
Table 3
.
Table 4
shows the
correlations between all latent variables.
3.4. Model 1. Cannabis use and internalizing behaviour problems
The independence model testing the hypothesis that all
cannabis scores and internalizing behaviour scores were
uncor-related was rejected:
2(30,
N
= 1,449) = 56.4,
p
< .003. The model
provided an acceptable fit to the data (
CFI
= .99,
RMSEA
= .03).
However, as shown in
Table 3
, correlations between
internaliz-ing behaviour problems (T1-2-3) and cannabis use (T2-T3) ranged
from .02 to .06 and thus are very small. Although these
corre-lations were significant (probably due to the large sample size),
they were indicative of non-relationships between cannabis use
and internalizing behaviour. This was confirmed by the Wald test.
Dropping parameters indicative of associations between
inter-nalizing behaviour (T1, T2 and T3) and cannabis use (T2 and
T3) resulted in a non-significant change of the model [
2(6,
N
= 1,449) = 11.2,
p
= .081]. Path-analysis revealed that although our
model represented the data well [
2(66,
N
= 1,449) = 215.2,
p
< .001;
Table 2
t-statistics of significant control variables (tobacco use and alcohol use) and inter-nalizing and exterinter-nalizing behaviour.
T2 tobacco use T3 tobacco use T2 alcohol use T3 alcohol use T1 Internalizing behaviour −3.2* −1.6 −.2 1.0 T2 Internalizing behaviour −3.7* −3.3* −.7 2.7* T3 Internalizing behaviour −4.2* −3.2* −.1 2.0 T1 Externalizing behaviour −6.1* −5.4* −4.2* −3.1* T2 Externalizing behaviour −11.6* −11.3* −9.2* −3.4* T3 Externalizing behaviour −10.3* −19.2* −7.8* −8.4* *p< .05.
Table 3
Factor loadings of the Indicators of the Latent variables of internalizing and exter-nalizing behaviour and cannabis use.
Variable Factor loadings
Internalizing behaviour and cannabis T1 Internalizing behaviour Anxious/Depressed .24 Withdrawn/Depressed .21* Somatic complaints .17* T2 Internalizing behaviour Anxious/Depressed .27* Withdrawn/Depressed .21* Somatic complaints .15* T2 Cannabis use Cannabis use 1.00 T3 Internalizing behaviour Anxious/Depressed .26* Withdrawn/Depressed .23* Somatic complaints .16* T3 Cannabis use Cannabis use 1.00
Externalizing behaviour and cannabis T1 Externalizing behaviour Aggressive behaviour 1.00 Rule-breaking behaviour .90* T2 Externalizing behaviour Aggressive behaviour 1.00* Rule-breaking behaviour 1.38* T2 Cannabis use Cannabis use 1.00 T3 Externalizing behaviour Aggressive behaviour 1.00 Rule-breaking behaviour 1.67* T3 Cannabis use Cannabis use 1.00 *p< .05
RMSEA
= .04,
CFI
= .97], all paths between internalizing (T1-2-3) and
cannabis use (T2-T3) were non-significant.
3.5. Model 2. Cannabis use and externalizing behaviour problems
The independence model that tested the hypothesis that all
cannabis scores and externalizing behaviour scores were
uncorre-lated, was rejected:
2(9,
N
= 1,449) = 64.4,
p
< .001. Also, although
RMSEA was relatively high (.07), the CFI was .99 and therefore our
model provided an acceptable fit to the data. Correlations between
externalizing behaviour (T1-2-3) and cannabis use (T2-T3) ranged
from .19 to .58 and thus were indicative of a relationship between
externalizing behaviour problems and cannabis use (see
Table 4
).
Next, path analysis was performed to address the temporal order of
cannabis use and externalizing behaviour problems (
Fig. 1
), hereby
controlling for alcohol and tobacco use at T2 and T3.
Path analysis revealed that the model represented the data well
[
2(34,
N
= 1,449) = 270.2,
p
< .001;
RMSEA
= .07,
CFI
= .96]. The paths
between externalizing behaviour problems measured at T1, T2, and
T3 were all significant (T1-T2;
z
= 11.8,
p
< .05; T1-T3;
z
= 4.9,
p
< .05;
Table 4Correlations of all latent variables of the CFA.
T2 Cannabis use T3 Cannabis use Model 1 T1 Internalizing behaviour .06* −.04* T2 Internalizing behaviour .06* −.02* T3 Internalizing behaviour .05* .02* Model 2 T1 Externalizing behaviour .19* .23* T2 Externalizing behaviour .40* .38* T3 Externalizing behaviour .24* .58* *p< .05.
T2-T3;
z
= 11.5,
p
< .05). The path between cannabis use T2 and T3
was also significant (
z
= 5.4,
p
< .05). In addition, the paths between
externalizing behaviour and tobacco use were all significant (T2;
z
= 11.7,
p
< .05; T3;
z
= 16.9,
p
< .05). Also, the paths between
exter-nalizing behaviour and alcohol use were all significant (T2;
z
= 8.4,
p
< .05; T3;
z
= 6.6,
p
< .05). The same occurred with cannabis use,
where the paths between cannabis use and tobacco use were
sig-nificant at T2 (
z
= 17.8,
p
< .05) and T3 (
z
= 18.0,
p
< .05) and also with
alcohol use at T2 (
z
= 2.9,
p
< .05) and T3 (
z
= 5.7,
p
< .05). Moreover,
externalizing behaviour and cannabis use significantly correlated
at T2 (
r
= 0.19,
p
< .05) and T3 (
r
= 0.34,
p
< .05).
Externalizing behaviour at T1 significantly predicted cannabis
use at T2 (
z
= 3.8,
p
< .05) and T3 (
z
= 2.7,
p
< .05). Externalizing
behaviour at T2 also significantly predicted cannabis use at T3
(
z
= 4.0,
p
< .05). Cannabis use measured at T2 did not show
sig-nificant association with externalizing behaviour problems at T3
(
z
=
−
1.4,
p
> .05) (
Fig. 1
).
4. Discussion
In the present longitudinal study, 1,449 respondents were
fol-lowed from the age of 11 to 16 to assess the relationship between
both internalizing and externalizing problems and cannabis use.
Two different hypotheses, the damage hypothesis and the
self-medication hypothesis, were tested using path analyses, thereby
controlling for possible confounding factors.
First, our data showed that cannabis use is strongly related to
externalizing behaviour problems in early adolescence, including
aggressive and delinquent behaviour. This result is largely in
agree-ment with previous studies (
Fergusson et al., 2007; Fergusson et al.,
2002; Khantzian, 1985; Monshouwer et al., 2006
). As expected,
our data supported the self-medication hypothesis, indicating that
externalizing problems precede cannabis use during adolescence
and not the other way around. Specifically, in our study,
external-izing problems at age 11 were associated with cannabis use at age
13 and age 16. Also, externalizing behaviour at age 13 predicted
cannabis use at age 16.
These results are in agreement with a number of other studies.
King et al. (2004)
, for example, also showed that externalizing
psy-chopathology at age 11 predicted cannabis use at age 14, although
it did not take into account potential confounders, such as the use
of other substances.
Korhonen et al. (2010)
recently showed that
early onset of smoking predicts cannabis initiation, while
control-ling for co-occurring externalizing behaviour problems. Whereas
Korhonen et al. (2010)
focused specifically on whether time of
smoking initiation was predictive of the onset of cannabis use,
we focused on the temporal order of cannabis use and
exter-nalizing behaviour problems. Although this study therefore had
a different focus compared to the present study, it does
illus-trate the importance of controlling for potentially confounding
factors when investigating cannabis-behaviour associations (or of
controlling for behaviour when studying associations between
spe-cific environmental factors and cannabis use). Another longitudinal
study (spanning 25 years) that did control for confounding
fac-tors demonstrated that conduct disorders at even a younger age
(7–9 years) were related to later substance use, including cannabis
use (
Fergusson et al., 2007
). Also,
Pedersen et al. (2001)
, confirmed
that conduct disorder at a young age is strongly associated with
cannabis use in young teenagers. All these studies supported results
that externalizing problems precede cannabis use. For the present
study as well as earlier studies, it should be noted that externalizing
behaviour explained only part of the variance of cannabis use,
indi-cating that other factors are also important correlates of cannabis
use during adolescence. Examples of such factors may be
sub-stance using peers and family functioning (e.g.
Coffey et al., 2000;
X1
Externalizing
Externalizing
X3
X2
Externalizing
T1
T2
T3
.37
.33
.10
.07
.11
.34
.13
D1D2
.82
.76
E7
E8
1.0
1.0
X5
Cannabis use
X4
Cannabis use
Agressive
behaviour
Rule breaking
behaviour
Agressive
behaviour
Rule breaking
behaviour
Agressive
Behaviour
Rule breaking
behaviour
E1
E2
E3
E4
E5
E6
.19
1.0
1.0
1.6
1.0 1.0
1.0 1.0
1.0 1.0
.90
1.39
1.0
.12
Tobacco
use
Tobacco
use
Tobacco
use
Alcohol
use
Tobacco
use
Alcohol
use
Alcohol
use
Alcoho
l use
.43
.07
.42
.13
.14
.42
.34 .21
Fig. 1.Path analysis of externalizing behaviour and cannabis use in young adolescence after controlling for tobacco and alcohol use, measured at both T2 and T3. All non-significant paths have been removed from the full model. Latent variables are shown in ellipses, and observed variables are shown in rectangles. Dependent variables have residuals (are not perfectly related to the other variables in the model) indicated by E’s (errors) pointing to measured variables and D’s (disturbances) pointing to latent variables. Each measured variable has an error path leading to it and each latent variable has a disturbance path leading to it. Equations of dependent variables: X2 (T2 externalizing behaviour) = .34(T2 tobacco use) + .21(T2 alcohol use) + .37X1(T1 externalizing behaviour) + .82D1 X3 (T3 externalizing behaviour) = .42 (T3 tobacco use) + .14 (T3 alcohol use) + .12X1 (T1 externalizing behaviour) + .33X2 (T2 externalizing behaviour) + .76D2 X4 (T2 cannabis use) = .43 (T2 tobacco use) + .07 (T2 alcohol use) + .10X1 (T1 externalizing behaviour) + 1.0E7 X5 (T3 cannabis use) = .42 (T3 tobacco use) + .13 (T3 alcohol use) + .07X1 (T1 externalizing behaviour) + .11X1 (T2 externalizing behaviour) + 1.0E7.
Fergusson and Horwood, 1997
). In addition, considering the
con-current correlations of cannabis use and externalizing behaviour at
different measurement points we cannot rule out reciprocal
rela-tions between the two, i.e. lagged associarela-tions remain possible
(
Fergusson et al., 2005
). Nonetheless, some evidence is provided
here that such lagged associations start with the presence of
exter-nalizing behaviour, as there was negligible cannabis use at T1, while
there was externalizing behaviour at that time.
Although evidence of damaging effects of cannabis has been
pro-vided in other studies (
Kandel et al., 1986; Kandel et al., 1992
),
our study did not support this hypothesis. This could be due to
the fact that the sample was quite young and had not been using
cannabis for a long period of time. Indeed, studies providing
evi-dence for damaging effects of cannabis observed these effects in
young adulthood (
Fergusson et al., 2002; White et al., 1999
).
Pos-sibly, such effects will also become evident in our sample at a later
stage. For now, however, it should be concluded that externalizing
problems at age of 11 and 13 predict cannabis use at later ages. If
the self-medication hypothesis is true, as the evidence suggests, it
would be good to know in more detail which aspects of
external-izing behaviour elicit the need for “medication”. One explanation
could be that those who show externalizing problems at age 11 use
cannabis to get rid of feelings of hostility or anger. If the
tempo-ral order is not the consequence of some form of self-medication,
a possible explanation is that cannabis use is a form of sensation
seeking behaviour, which has regularly been identified as a
charac-teristic of externalizing behaviour (
Huizink et al., 2006; Marsman
et al., 2008; Raine, 1996
). There may be several mediating factors
explaining the temporal order with externalizing problems
pre-ceding cannabis use as well. Examples include exclusion from peer
groups that show less experimental behaviour and inclusion in peer
groups showing increased levels of experimental behaviour among
individuals characterized by externalizing behaviours (
Coffey et al.,
2000; Fergusson and Horwood, 1997
).
With respect to internalizing behaviour problems, our study did
not confirm the results of several earlier studies that did find
associ-ations with cannabis use (
Degenhardt et al., 2001; Degenhardt et al.,
2003; Patton et al., 2002; Hayatbakhsh et al., 2007a
). It should be
noted that generally the relations between cannabis use and
inter-nalizing behaviour have been weaker than those with exterinter-nalizing
behaviour, and that existing associations could often be accounted
for by co-occurring risk factors such as sociodemographic factors
and use of other substances (
Moore et al., 2007
). Our results are
in agreement with those studies not finding an association at all
(
Monshouwer et al., 2006; Harder et al., 2008; McGee et al., 2000
).
A possible explanation for these mixed results might be that
stud-ies that did find significant associations focused mainly on older
individuals (
Brook et al., 1998; Hayatbakhsh et al., 2007a; Patton
et al., 2002; van Laar et al., 2007; Wittchen et al., 2007
), although
there is evidence opposing this hypothesis as well (
Hayatbakhsh
et al., 2008
). For example,
Hayatbakhsh et al. (2007a)
showed, using
logistic regression analysis, that cannabis use at the age of 15 was
associated with an increased risk for Anxiety and depression at the
age of 21. One study providing compelling evidence in favour of
the hypothesis was performed by
Arseneault et al. (2002)
, who
concluded that the association between cannabis use and
depres-sive symptoms was age dependent, following findings showing that
cannabis use at age 15 was not associated with depressive
symp-toms at age 26 while cannabis use at age 18 was.
Hayatbakhsh et al.
(2007a)
suggested that the association is not only dependent on
age, but also on duration and frequency; only those who already
started cannabis use at age 15 and using it frequently until the age
of 21 showed elevated levels of anxiety and depression in young
adulthood. The fact that internalizing problems are more evident
in late adolescence and young adulthood than in early adolescence
may also play a significant role (
Kessler et al., 2007
).
The present study has a number of limitations. One limitation
is that mental health and cannabis use data were obtained from
self-reports. Use of multiple informants, particularly concerning
mental health, would have been preferable (
Offord et al., 1996
).
Despite the fact that previous studies have concluded that self
reporting on substance use is generally valid (
Buchan et al., 2002
)
(and the fact that cannabis use in The Netherlands is not illegal,
which possibly allows more honest answers), one could still argue
that the nature of the questions might have led to socially-desirable
answers (especially for young adolescents). Another limitation is
the loss of respondents between measurement 1 and 3, especially
since non-responders differed from responders in terms of SES and
gender. However, it can be argued that if non responders would
have been included in the present analysis, the present results
would have strengthened, since it can be presumed that more
cannabis users would be present among the non-responders. On the
other hand, it can also be argued that the present results would have
been weakened when non-responders (with lower SES) would have
been included in the present analysis. SES could have explained a
greater part of the variance of cannabis use, which in turn could
have weakened the variance explained by externalizing behaviour.
Lastly, despite the fact that we controlled for several important
con-founders, it cannot be ruled out that our results can be explained
by non-observed confounding factors (thus supporting the
shared-causes hypothesis). For example, it has been shown that genetic
factors are important determinants of both externalizing behaviour
problems and cannabis use (
Kendler et al., 2000; Lynskey et al.,
2002; Rutter et al., 1999
). Research using twin designs has also
identified common genetic factors of externalizing problems and
substance use behaviour during adolescence (
Shelton et al., 2007;
Young et al., 2000
). For this study, we only had proxy variables of
genetic confounding available (i.e. those constituting familial risk
of internalizing and externalizing behaviour as well as substance
use). There are also several environmental factors (e.g. family
func-tioning, peer group influences) that could not be incorporated in
this study.
Despite some clear limitations, it may be noted that this study
is one of the few prospective studies focusing on cannabis use
and both internalizing and externalizing problems that was able
to incorporate data assessed before cannabis initiation, allowing
testing of both the damage and the self-medication
hypothe-ses. Whereas externalizing problems at age 11 and 13 preceded
cannabis use at age 13 and 16, cannabis use did not precede
exter-nalizing problems at any age. Future research should focus on a
broader age span and use longer follow-up periods to investigate
relationships with mental health problems (both internalizing and
externalizing) more thoroughly.
Role of the funding source
TRAILS has been financially supported by various grants
from the Netherlands Organization for Scientific Research NWO
(Medical Research Council program grant GB-MW 940-38-011;
ZonMW Brainpower grant 100-001-004; ZonMw Risk Behaviour
and Dependence grants 60-60600-98-018 and
60-60600-97-118; ZonMw Culture and Health grant 261-98-710; Social
Sciences Council medium-sized investment grants GB-MaGW
480-01-006 and GB-MaGW 480-07-001; Social Sciences
Coun-cil project grants GB-MaGW 457-03-018, GBMaGW 452-04-314,
and GB-MaGW 452-06-004; NWO large-sized investment grant
175.010.2003.005); the Sophia Foundation for Medical Research
(projects 301 and 393), the Dutch Ministry of Justice (WODC), and
the participating universities.
Contributors
MG and AM performed statistical analysis. MG, SH, HS and WV
drafted the manuscript and designed the study and participated
in discussing the results and revised the manuscript. All authors
contributed to and commented on final draft and have approved
the final manuscript.
Conflict of interest
All authors declare that they have no conflicts of interests in
connection with any aspect of the research.
Acknowledgements
This research is part of the TRacking Adolescents’ Individual
Lives Survey (TRAILS). Participating centers of TRAILS include
vari-ous departments of the University Medical Center and University of
Groningen, the Erasmus University Medical Center Rotterdam, the
University of Utrecht, the Radboud Medical Center Nijmegen, and
the Trimbos Institute, all in the Netherlands. Principal investigators
are Prof. Dr. J. Ormel (University Medical Center Groningen) and
Prof. Dr. F.C. Verhulst (Erasmus University Medical Center). We are
grateful to all adolescents, their parents and teachers who
partici-pated in this research and to everyone who worked on this project
and made it possible.
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